Though the previous models improved the objects' boundary, we need models that observe larger regions.
Thus, multi-scales models emerged [1]}, [2]}, [3]}.
They obtain a full semantic map in low-resolution (coarse prediction map), then refine it with different fusion operations, e.g., fusion cascade [4]}, attention blocks [5]}, [6]}, layer aggregation [7]}, residual units [8]}, and gated fusion [9]}.
These models are unnecessarily complex to extract robust features.
Instead, we use auxiliary tasks to reinforce the gradient and achieve better information extraction.
